Docker: You are Doing MCP Wrong: 3 Big Misconceptions

Sep 3, 2025

—

Source URL: https://www.docker.com/blog/mcp-misconceptions-tools-agents-not-api/
Source: Docker
Title: You are Doing MCP Wrong: 3 Big Misconceptions

Feedly Summary: MCP is not an API. Tools are not agents. MCP is more than tools. Here’s what this means in practice. Most developers misread the Model Context Protocol because they map it onto familiar API mental models. That mistake breaks agent designs, observability, and the “last mile” where non-deterministic reasoning must meet deterministic execution. This piece…

AI Summary and Description: Yes

Summary: The text discusses the Model Context Protocol (MCP), clarifying common misconceptions about its nature and function as compared to traditional API frameworks. By emphasizing the distinction between tools and agents, it offers practical recommendations for developers in AI, particularly in the context of large language models (LLMs), detailing effective design patterns while outlining pitfalls to avoid.

Detailed Description:

The text provides a thorough exploration of the Model Context Protocol (MCP), addressing misconceptions that may hinder developers’ understanding and implementation of its mechanisms in AI applications.

Key Points:
– **MCP vs. API Misconception**:
– MCP is not simply another API like REST or gRPC. Instead, it serves as a model-facing protocol that enhances the usage of tools, intent mediation, and context exchange for large language model (LLM) applications.
– Misunderstanding arises from developers applying familiar API paradigms to MCP, leading to ineffective agent designs and experiences.

– **Understanding Tools vs. Agents**:
– Tools are execution entities; they take inputs and provide outputs whereas agents are responsible for planning, adaptive decision-making, and evaluation.
– The confusion often stems from simplified presentations of AI that suggest a single call can encapsulate all functionalities. The distinction is critical for developing sophisticated AI systems.

– **Core Functions of MCP**:
– Incorporates tool definitions that include intent and affordances, enriching the interaction with models beyond mere request-response cycles.
– Introduces a combination of structured resources, prompts, and human elicitation strategies to facilitate comprehensive interaction and decision-making in non-deterministic AI environments.

– **Design Patterns and Anti-Patterns**:
– Advocates for wrapping traditional business APIs with MCP tool definitions that clarify success criteria and execution parameters.
– Highlights design patterns that foster effective tool and agent collaboration while noting common anti-patterns that can compromise system reliability, such as expecting tools to function with complex state changes without guardrails.

– **Implementation Considerations**:
– Advises creating an architecture where non-deterministic planning works seamlessly with deterministic execution.
– Stresses the importance of thorough observability and governance practices, ensuring all interactions are traceable for trust and auditing purposes.

– **Checklist for Developers**:
– The text presents actionable checklists for defining agent behavior, managing resources, and establishing audit trails, thus facilitating reliable implementations.

In conclusion, understanding MCP’s nuances fosters the development of robust AI applications, addressing common pitfalls while promoting effective practices for integrating complex agent designs with reliable execution methodologies. This is particularly significant for security and compliance professionals striving to build and manage trustworthy AI systems.

3 a Act actions adaptive age agent agent behavior agent collaboration agents AGI AI AI applications AI systems All and anti API APIs app Application applications Arch architecture art as at ated audit audit trail audit trails auditing Behavior beyond Bi business by C CI co Col collaboration common pitfalls compliance compliance professionals concept Context core criteria critical D de decision decision-making DeFi definition definitions design design patterns deterministic developer developers development Docker e effective end environment environments evaluation execution exp experience exploration for framework frameworks function g Gen Go governance governance practices Guardrails H high Highlight http HTTPS human implementation implementation considerations in intent inter interaction interactions io Iron ite k Key l Labor language language model language models large large language model Large Language Model (LLM) large language models Large Language Models (LLMs) leading led Li liability llm llms lm M making man mcp mean media methodologies mini misconceptions ML Mode model model context protocol models N nation NCA NIST no non NPU o observability of off on ons OPM oS other out output Outputs over paradigms parameter patterns per phi pitfalls planning point practices pre pro professionals prompt prompts protocol ps Q R rate RCE re reasoning recommendations red reliability resource resources response responsible Ro RoT RPC Rust s sec security security and compliance side Sig Sim single source SRE SSE SSL state strategies structured system system reliability systems T ted text the to tool tool definitions tools TP trust trustworthy AI two UI under US usage use V val Valuation Wi x z